Worldwide Universities Network (WUN) Web Observatory: Applying Lessons from the Web to Transform the Research Data Ecosystem

Simon Price; Wendy Hall; Graeme Earl; Thanassis Tiropanis; Ramine Tinati; Xin Wang; Eleonora Gandolfi; Jane Gatewood; Richard Boateng; David Denemark; Alexander Groflin; Brian Loader; Maxine Schmidt; Marilyn Billings; Gerasimos Spanakis; Hussein Suleman; Kelvin Tsoi; Bridgette Wessels

doi:10.1145/3041021.3051691

Worldwide Universities Network (WUN) Web Observatory: Applying Lessons from the Web to Transform the Research Data Ecosystem

Simon Price, Wendy Hall, Graeme Earl, Thanassis Tiropanis, Ramine Tinati, Xin Wang, Eleonora Gandolfi, Jane Gatewood, Richard Boateng, David Denemark, Alexander Groflin, Brian Loader, Maxine Schmidt, Marilyn Billings, Gerasimos Spanakis, Hussein Suleman, Kelvin Tsoi, Bridgette Wessels

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

2 Citations (Scopus)

440 Downloads (Pure)

Abstract

The ongoing growth in research data publication supports global intra-disciplinary and inter-disciplinary research collaboration but the current generation of archive-centric research data repositories do not address some of the key practical obstacles to research data sharing and re-use, specifically: discovering relevant data on a global scale is time-consuming; sharing "live" and streaming data is non-trivial; managing secure access to sensitive data is overly complicated; and, researchers are not guaranteed attribution for re-use of their own research data. These issues are keenly felt in an international network like the Worldwide Universities Network (WUN) as it seeks to address major global challenges. In this paper we outline the WUN Web Observatory project's plan to overcome these obstacles and, given that these obstacles are not unique to WUN, we also propose an ambitious, longer-term route to their solution at Web-scale by applying lessons from the Web itself.

Original language	English
Title of host publication	WWW '17 Companion
Subtitle of host publication	Proceedings of the 26th International Conference on World Wide Web Companion
Editors	Rick Barrett, Rick Cummings
Place of Publication	Perth, Australia
Pages	1665-1667
Number of pages	3
DOIs	https://doi.org/10.1145/3041021.3051691
Publication status	Published - 3 Apr 2017
Event	Workshop on Web Observatories, Social Machines and Decentralisation - PCEC, Perth, Australia Duration: 3 Apr 2017 → … http://sociam.org/wow2017/

Workshop

Workshop	Workshop on Web Observatories, Social Machines and Decentralisation
Abbreviated title	WOW17
Country/Territory	Australia
City	Perth
Period	3/04/17 → …
Internet address	http://sociam.org/wow2017/

Research Groups and Themes

WUN
Big Data
Data Science

Keywords

Research Data Management
Data Science
Social Machines

Access to Document

10.1145/3041021.3051691Licence: Unspecified

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This is the final published version of the article (version of record). It first appeared online via International World Wide Web Conference Committee at http://dl.acm.org/citation.cfm?id=3051691. Please refer to any applicable terms of use of the publisher.
Final published version, 1.59 MBLicence: CC BY

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Price, S., Hall, W., Earl, G., Tiropanis, T., Tinati, R., Wang, X., Gandolfi, E., Gatewood, J., Boateng, R., Denemark, D., Groflin, A., Loader, B., Schmidt, M., Billings, M., Spanakis, G., Suleman, H., Tsoi, K., & Wessels, B. (2017). Worldwide Universities Network (WUN) Web Observatory: Applying Lessons from the Web to Transform the Research Data Ecosystem. In R. Barrett, & R. Cummings (Eds.), WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion (pp. 1665-1667). https://doi.org/10.1145/3041021.3051691

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abstract = "The ongoing growth in research data publication supports global intra-disciplinary and inter-disciplinary research collaboration but the current generation of archive-centric research data repositories do not address some of the key practical obstacles to research data sharing and re-use, specifically: discovering relevant data on a global scale is time-consuming; sharing {"}live{"} and streaming data is non-trivial; managing secure access to sensitive data is overly complicated; and, researchers are not guaranteed attribution for re-use of their own research data. These issues are keenly felt in an international network like the Worldwide Universities Network (WUN) as it seeks to address major global challenges. In this paper we outline the WUN Web Observatory project's plan to overcome these obstacles and, given that these obstacles are not unique to WUN, we also propose an ambitious, longer-term route to their solution at Web-scale by applying lessons from the Web itself.",

keywords = "Research Data Management, Data Science, Social Machines",

author = "Simon Price and Wendy Hall and Graeme Earl and Thanassis Tiropanis and Ramine Tinati and Xin Wang and Eleonora Gandolfi and Jane Gatewood and Richard Boateng and David Denemark and Alexander Groflin and Brian Loader and Maxine Schmidt and Marilyn Billings and Gerasimos Spanakis and Hussein Suleman and Kelvin Tsoi and Bridgette Wessels",

year = "2017",

month = apr,

day = "3",

doi = "10.1145/3041021.3051691",

language = "English",

isbn = "9781450349147",

pages = "1665--1667",

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booktitle = "WWW '17 Companion",

note = "Workshop on Web Observatories, Social Machines and Decentralisation, WOW17 ; Conference date: 03-04-2017",

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Price, S, Hall, W, Earl, G, Tiropanis, T, Tinati, R, Wang, X, Gandolfi, E, Gatewood, J, Boateng, R, Denemark, D, Groflin, A, Loader, B, Schmidt, M, Billings, M, Spanakis, G, Suleman, H, Tsoi, K & Wessels, B 2017, Worldwide Universities Network (WUN) Web Observatory: Applying Lessons from the Web to Transform the Research Data Ecosystem. in R Barrett & R Cummings (eds), WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion. Perth, Australia, pp. 1665-1667, Workshop on Web Observatories, Social Machines and Decentralisation, Perth, Australia, 3/04/17. https://doi.org/10.1145/3041021.3051691

Worldwide Universities Network (WUN) Web Observatory: Applying Lessons from the Web to Transform the Research Data Ecosystem. / Price, Simon; Hall, Wendy; Earl, Graeme et al.
WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion. ed. / Rick Barrett; Rick Cummings. Perth, Australia, 2017. p. 1665-1667.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Worldwide Universities Network (WUN) Web Observatory

T2 - Workshop on Web Observatories, Social Machines and Decentralisation

AU - Price, Simon

AU - Hall, Wendy

AU - Earl, Graeme

AU - Tiropanis, Thanassis

AU - Tinati, Ramine

AU - Wang, Xin

AU - Gandolfi, Eleonora

AU - Gatewood, Jane

AU - Boateng, Richard

AU - Denemark, David

AU - Groflin, Alexander

AU - Loader, Brian

AU - Schmidt, Maxine

AU - Billings, Marilyn

AU - Spanakis, Gerasimos

AU - Suleman, Hussein

AU - Tsoi, Kelvin

AU - Wessels, Bridgette

PY - 2017/4/3

Y1 - 2017/4/3

N2 - The ongoing growth in research data publication supports global intra-disciplinary and inter-disciplinary research collaboration but the current generation of archive-centric research data repositories do not address some of the key practical obstacles to research data sharing and re-use, specifically: discovering relevant data on a global scale is time-consuming; sharing "live" and streaming data is non-trivial; managing secure access to sensitive data is overly complicated; and, researchers are not guaranteed attribution for re-use of their own research data. These issues are keenly felt in an international network like the Worldwide Universities Network (WUN) as it seeks to address major global challenges. In this paper we outline the WUN Web Observatory project's plan to overcome these obstacles and, given that these obstacles are not unique to WUN, we also propose an ambitious, longer-term route to their solution at Web-scale by applying lessons from the Web itself.

AB - The ongoing growth in research data publication supports global intra-disciplinary and inter-disciplinary research collaboration but the current generation of archive-centric research data repositories do not address some of the key practical obstacles to research data sharing and re-use, specifically: discovering relevant data on a global scale is time-consuming; sharing "live" and streaming data is non-trivial; managing secure access to sensitive data is overly complicated; and, researchers are not guaranteed attribution for re-use of their own research data. These issues are keenly felt in an international network like the Worldwide Universities Network (WUN) as it seeks to address major global challenges. In this paper we outline the WUN Web Observatory project's plan to overcome these obstacles and, given that these obstacles are not unique to WUN, we also propose an ambitious, longer-term route to their solution at Web-scale by applying lessons from the Web itself.

KW - Research Data Management

KW - Data Science

KW - Social Machines

UR - http://www.sociam.org/wow2017

U2 - 10.1145/3041021.3051691

DO - 10.1145/3041021.3051691

M3 - Conference Contribution (Conference Proceeding)

SN - 9781450349147

SP - 1665

EP - 1667

BT - WWW '17 Companion

A2 - Barrett, Rick

A2 - Cummings, Rick

CY - Perth, Australia

Y2 - 3 April 2017

ER -

Price S, Hall W, Earl G, Tiropanis T, Tinati R, Wang X et al. Worldwide Universities Network (WUN) Web Observatory: Applying Lessons from the Web to Transform the Research Data Ecosystem. In Barrett R, Cummings R, editors, WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion. Perth, Australia. 2017. p. 1665-1667 doi: 10.1145/3041021.3051691

Worldwide Universities Network (WUN) Web Observatory: Applying Lessons from the Web to Transform the Research Data Ecosystem

Abstract

Workshop

Research Groups and Themes

Keywords

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